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arXiv · 1908.03385

Gradient Boosting Survival Tree with Applications in Credit Scoring

Miaojun Bai·Yan Zheng·Yun Shen
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Abstract

Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonparametric ensemble tree model called gradient boosting survival tree (GBST) that extends the survival tree models with a gradient boosting algorithm. The survival tree ensemble is learned by minimizing the negative log-likelihood in an additive manner. The proposed model optimizes the survival probability simultaneously for each time period, which can reduce the overall error significantly. Finally, as a test of the applicability, we apply the GBST model to quantify the credit risk with large-scale real market datasets. The results show that the GBST model outperforms the existing survival models measured by the concordance index (C-index), Kolmogorov-Smirnov (KS) index, as well as by the area under the receiver operating characteristic curve (AUC) of each time period.

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Miaojun Bai, Yan Zheng, Yun Shen. 2021-04-12. Gradient Boosting Survival Tree with Applications in Credit Scoring. https://doi.org/10.1080/01605682.2021.1919035

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